Many computer vision applications, such as motion tracking, image recognition, and three-dimensional modeling, involve detecting and describing portions of an image. In doing so, it has become popular to use local image feature descriptors such as provided via the scale-invariant feature transform (SIFT) method. In this method, various local image features within an image are detected and described. In particular, an interest point detector selects interest points indicative of the content of the image while a local image feature descriptor describes the characteristics of each interest point and its neighborhood (i.e., a local image patch). Among other features, a local image feature descriptor indicates oriented gradients for the interest point and its neighborhood. As one aspect of characterizing the local features of an image, the oriented gradients of the local image patch are mapped according to a probabilistic model, such as a histogram which represents a data distribution of the oriented gradients.
Despite the general utility of local image feature descriptors in representing local features of an image, some computer vision applications perform less admirably than desired.